Browsing by Author "Yazici, Cemal"

Now showing 1 - 6 of 6
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    A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging
    (Wolters Kluwer, 2023) Yao, Lanhong; Zhang, Zheyuan; Keles, Elif; Yazici, Cemal; Tirkes, Temel; Bagco, Ulas; Radiology and Imaging Sciences, School of Medicine
    Purpose of review: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Recent findings: This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. Summary: Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.
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    Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium
    (Wolters Kluwer, 2022) Tirkes, Temel; Chinchilli, Vernon M.; Bagci, Ulas; Parker, Jason G.; Zhao, Xuandong; Dasyam, Anil K.; Feranec, Nicholas; Grajo, Joseph R.; Shah, Zarine K.; Poullos, Peter D.; Spilseth, Benjamin; Zaheer, Atif; Xie, Karen L.; Wachsman, Ashley M.; Campbell-Thompson, Martha; Conwell, Darwin L.; Fogel, Evan L.; Forsmark, Christopher E.; Hart, Phil A.; Pandol, Stephen J.; Park, Walter G.; Pratley, Richard E.; Yazici, Cemal; Laughlin, Maren R.; Andersen, Dana K.; Serrano, Jose; Bellin, Melena D.; Yadav, Dhiraj; Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC); Radiology and Imaging Sciences, School of Medicine
    This core component of the Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study will examine the hypothesis that advanced magnetic resonance imaging (MRI) techniques can reflect underlying pathophysiologic changes and provide imaging biomarkers that predict diabetes mellitus (DM) following acute pancreatitis (AP). A subset of participants in the DREAM study will enroll and undergo serial MRI examinations using a specific research protocol. We aim to differentiate at-risk individuals from those who remain euglycemic by identifying parenchymal features following AP. Performing longitudinal MRI will enable us to observe and understand the natural history of post-AP DM. We will compare MRI parameters obtained by interrogating tissue properties in euglycemic, prediabetic and incident diabetes subjects and correlate them with metabolic, genetic, and immunological phenotypes. Differentiating imaging parameters will be combined to develop a quantitative composite risk score. This composite risk score will potentially have the ability to monitor the risk of DM in clinical practice or trials. We will use artificial intelligence, specifically deep learning, algorithms to optimize the predictive ability of MRI. In addition to the research MRI, the DREAM study will also correlate clinical computerized tomography and MRI scans with DM development.
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    Radiomics Boosts Deep Learning Model for IPMN Classification
    (Springer, 2023) Yao, Lanhong; Zhang, Zheyuan; Demir, Ugur; Keles, Elif; Vendrami, Camila; Agarunov, Emil; Bolan, Candice; Schoots, Ivo; Bruno, Marc; Keswani, Rajesh; Miller, Frank; Gonda, Tamas; Yazici, Cemal; Tirkes, Temel; Wallace, Michael; Spampinato, Concetto; Bagci, Ulas; Radiology and Imaging Sciences, School of Medicine
    Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.
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    Rationale and Design for the Diabetes RElated to Acute Pancreatitis and Its Mechanisms (DREAM) Study: A Prospective Cohort Study From the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC)
    (Wolters Kluwer, 2022) Hart, Phil A.; Papachristou, Georgios I.; Park, Walter G.; Dyer, Anne-Marie; Chinchilli, Vernon M.; Afghani, Elham; Akshintala, Venkata S.; Andersen, Dana K.; Buxbaum, James L.; Conwell, Darwin L.; Dungan, Kathleen M.; Easler, Jeffrey J.; Fogel, Evan L.; Greenbaum, Carla J.; Kalyani, Rita R.; Korc, Murray; Kozarek, Richard; Laughlin, Maren R.; Lee, Peter J.; Maranki, Jennifer L.; Pandol, Stephen J.; Evans Phillips, Anna; Serrano, Jose; Singh, Vikesh K.; Speake, Cate; Tirkes, Temel; Toledo, Frederico G. S.; Trikudanathan, Guru; Vege, Santhi Swaroop; Wang, Ming; Yazici, Cemal; Zaheer, Atif; Forsmark, Christopher E.; Bellin, Melena D.; Yadav, Dhiraj; Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC); Medicine, School of Medicine
    Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM after AP and understanding the underlying mechanisms. The DREAM (Diabetes RElated to Acute Pancreatitis and its Mechanisms) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM after AP. In the following article, we summarize literature regarding the epidemiology of DM after AP and provide the rationale and an overview of the DREAM study.
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    Recruitment and Retention Strategies for the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium
    (Wolters Kluwer, 2022) Yazici, Cemal; Dyer, Anne-Marie; Conwell, Darwin L.; Afghani, Elham; Andersen, Dana K.; Basina, Marina; Bellin, Melena D.; Boone, Leslie R.; Casu, Anna; Easler, Jeffrey J.; Greenbaum, Carla J.; Hart, Phil A.; Jeon, Christie Y.; Lee, Peter J.; Meier, Shelby; Papachristou, Georgios I.; Raja-Khan, Nazia T.; Saeed, Zeb I.; Serrano, Jose; Yadav, Dhiraj; Fogel, Evan L.; Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAP); Medicine, School of Medicine
    Recruitment and retention of patients with acute pancreatitis (AP) in clinical studies can be challenging. While some obstacles are similar to other clinical conditions, some are unique to AP. Identifying potential barriers early and developing targeted solutions can help optimize recruitment and retention in AP studies. Such preemptive and detailed planning can help prospective, longitudinal studies focusing on exocrine and endocrine complications of AP in accurately measuring outcomes. This manuscript highlights the challenges in recruitment and retention strategies in AP studies and reviews available resources to create opportunities to address them. We describe the multifaceted approach used by the Recruitment and Retention Committee of the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC), which builds upon earlier experiences to develop a recruitment and retention plan for the DREAM (Diabetes RElated to Acute pancreatitis and its Mechanisms) study.
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    Standard Operating Procedures for Biospecimen Collection, Processing, and Storage: From the Type 1 Diabetes in Acute Pancreatitis Consortium
    (Wolters Kluwer, 2022) Wasserfall, Clive; Dyer, Anne-Marie; Speake, Cate; Andersen, Dana K.; Baab, Kendall Thomas; Bellin, Melena D.; Broach, James R.; Campbell-Thompson, Martha; Chinchilli, Vernon M.; Lee, Peter J.; Park, Walter G.; Pratley, Richard E.; Saloman, Jami L.; Sims, Emily K.; Tang, Gong; Yadav, Dhiraj; Yazici, Cemal; Conwell, Darwin L.; Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC); Pediatrics, School of Medicine
    Differences in methods for biospecimen collection, processing, and storage can yield considerable variability and error. Therefore, best practices for standard operating procedures are critical for successful discovery, development, and validation of disease biomarkers. Here, we describe standard operating procedures developed for biospecimen collection during the DREAM (Diabetes RElated to Acute pancreatitis and its Mechanisms) Study within the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC). Notably these protocols were developed using an integrative process based on prior consortium experience and with input from working groups with expertise in immunology, pancreatitis and diabetes. Publication and adoption consistent biospecimen protocols will inform future studies and allow for better comparisons across different metabolic research efforts.